Method and system for managing service quality according to network status predictions

ABSTRACT

Aspects of the subject disclosure may include, for example, obtaining predicted available bandwidths for an end user device, monitoring buffer occupancy of a buffer of the end user device, determining bit rates for portions of media content according to the predicted available bandwidths and according to the buffer occupancy, and adjusting bit rates for portions of media content according to the predicted available bandwidths and according to the buffer occupancy during streaming of the media content to the end user device over a wireless network. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/114,817, filed Feb. 11, 2015, the disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a method and system for managingservice quality according to network status predictions.

BACKGROUND

Video streaming can make up a large portion of traffic in cellularnetworks. Increasing numbers of users are watching videos on theircellular devices. Recent studies show that mobile video accounts formore than 50% of the cellular traffic today and is expected to growfourteen fold by 2018.

A recent study shows that between 40% and 73% of all videos played onmobile networks experience stalls. For every 60 seconds of video, userson 3G networks have experienced an average of 47 seconds of stall, whilethose on 4G networks have experienced 15 seconds of stall.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 depicts an illustrative embodiment of a system that providescommunication services including video streaming;

FIG. 2 depicts another illustrative embodiment of a system that providescommunication services including video streaming;

FIG. 3 depicts an illustrative embodiment of a datagram that can be usedfor exposing key performance indicators including predicted availablebandwidth;

FIG. 4 depicts an illustrative embodiment of a method used in portionsof the systems described in FIGS. 1-2;

FIG. 5 depicts an illustrative embodiment of key performance indicatorsthat can be utilized in the method of FIG. 4 and in portions of thesystems described in FIGS. 1-2;

FIGS. 6-7 depict an illustrative embodiment of predictions that can bemade in association with the method of FIG. 4 and in portions of thesystems described in FIGS. 1-2;

FIG. 8 depicts an illustrative embodiment of variables and parametersthat can be utilized in optimization in association with the method ofFIG. 4 and in portions of the systems described in FIGS. 1-2 accordingto a first rate selection algorithm;

FIG. 9 depicts an illustrative embodiment of a rate selection processthat can be used with the method of FIG. 4 and in portions of thesystems described in FIGS. 1-2 utilizing the first rate selectionalgorithm;

FIGS. 10-13 depict an illustrative embodiment of a comparison between anexemplary bit rate selection process utilizing the first rate selectionalgorithm and other streaming algorithms;

FIG. 14 depicts an illustrative embodiment of a communication device;

FIG. 15 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed, maycause the machine to perform any one or more of the methods describedherein;

FIG. 16 depicts an illustrative embodiment of variables and parametersthat can be utilized in estimating the performance of an exemplary rateselection algorithm that has information of all future events. Thislevel of information may not be available in practice but this exemplaryalgorithm serves as a baseline to judge the performance of the proposedinvention described in FIG. 21.

FIG. 17 depicts performance comparison of the exemplary algorithm withtwo prior known algorithms.

FIGS. 18-20 depict performance of another rate selection algorithm.

FIG. 21 depicts an illustrative embodiment of another rate selectionprocess that can be used with the process associated with FIG. 16;

FIG. 22 depicts results of implementation of an exemplary PBA process;and

FIGS. 23-24 depict an illustrative embodiment of a comparison between anexemplary bit rate selection process and other streaming algorithms.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for dynamically adjusting bit rates during streaming ofcontent, where the adjustments are based on predicted availablebandwidth for the end user device. Other embodiments are described inthe subject disclosure.

One or more aspects of the subject disclosure include systems andmethods that provide for utilizing information used by video adaptationprocesses along with available network bandwidth to manage streaming ofcontent to end user devices. The content can be video content, such asmovies, video conferences, and so forth. The management of the streamingprocess can include dynamically adjusting bit rates during thestreaming. In one embodiment, the adaptation process performed by avideo client can determine a video quality for each chunk of content.For example, the adaptation process can obtain more accurate estimationsof available bandwidth for an end user device by relying upon aprediction of available bandwidth over a future time period that isbased on a network service provider's monitoring of network conditions.The available bandwidth predictions can then be utilized for adjustingbit rates during the streaming of content over a network (e.g., acellular wireless network) to an end user device.

One or more of the exemplary embodiments can shift the task of bandwidthestimation from the client to the network service provider. As anexample, the network service provider (e.g., through an ApplicationProgramming Interface (API)) can provide a client's media player with aprediction of bandwidth available to that client for a future timeduration (e.g., seconds). A network service provider can have a globalview of all traffic that is flowing through their network, which mayinclude information such as an end users' signal strength, wirelesschannel quality, number of active users, users' mobility patterns, andso forth. Further, the network service provider can know how differentmiddle-boxes in the network will affect an end user device's trafficflow and will know of different priority treatments applicable to theend user device's traffic. By having visibility of competing flowsinside the network, the network service provider can more accuratelypredict each end user device's bandwidth for a future time period ascompared to bandwidth estimations made by a client (e.g. a media playerand/or a content provider) that has limited, if any, network conditioninformation. For instance, the network service provider can predict,using this information, how a scheduler operating inside a base stationwould rank the end user device's traffic and how much cellular resourceswould be allocated for that end user device, which can translate intoavailable bandwidth for the end user device.

One or more of the exemplary embodiments utilize dynamic bit rateadjustment during content streaming based on predicted availablebandwidth for the end user device which can be more desirable than lessaccurate predictions of bandwidth such as based on historicalthroughput. In wireless networks (e.g., cellular networks),historical-based estimates can vary significantly. There are manyfactors, including the end user device's signal strength, number ofusers in the cell, the congestion in the cell, and end user devicemobility that can determine an end user device's bandwidth. As a result,an end user device's bandwidth can vary widely even over short periodsof time, rendering any historical estimates as potentially inaccurate.

One embodiment of the subject disclosure includes a machine-readablestorage medium, comprising executable instructions that, when executedby a processor, facilitate performance of operations, includingobtaining a predicted available bandwidth for an end user device. Theprocessor can determine a buffer occupancy threshold for the end userdevice and determine a buffer occupancy of the end user device. Theprocessor can compare the buffer occupancy with the buffer threshold anddetermine a video bit rate for a portion of media content according tothe predicted available bandwidth for the end user device and accordingto the comparing of the buffer occupancy with the buffer threshold. Thevideo bit rate can be applied during streaming of the portion of themedia content to the end user device over a wireless network.

One embodiment of the subject disclosure is a method that includesobtaining, by a system including a network server, first performancedata associated with an end user device and second performance dataassociated with a cell of a wireless network in which the end userdevice is operating, where the first performance data includes channelquality indicator data, reference signal received quality data or acombination thereof, and where the second performance data includes cellcongestion data. The method includes determining, by the system, apredicted available bandwidth for the end user device according to thefirst and second performance data. The method includes receiving, by thesystem from a processor, a request for the predicted available bandwidthfor the end user device. The method includes providing, by the system tothe processor, the predicted available bandwidth for the end user deviceto cause a video bit rate to be determined for a portion of mediacontent according to the predicted available bandwidth for the end userdevice and according to a comparison of a buffer occupancy of the enduser device with a buffer threshold for the end user device. The methodincludes facilitating, by the system over the wireless network,streaming of the portion of the media content to the end user deviceutilizing the video bit rate.

One embodiment of the subject disclosure includes a system having aprocessor and a memory that stores executable instructions that, whenexecuted by the processor, facilitate performance of operations. Theprocessor can obtain predicted available bandwidths for an end userdevice and monitor buffer occupancy of a buffer of the end user device.The processor can determine bit rates for portions of media contentaccording to the predicted available bandwidths and according to thebuffer occupancy. The processor can adjust bit rates for portions ofmedia content according to the predicted available bandwidths andaccording to the buffer occupancy during streaming of the media contentto the end user device.

FIG. 1 depicts an illustrative embodiment of a communication system 100that provides communication services to communication devices (e.g., enduser device 101), including wireless communication services. The enduser devices 101 can be various types of devices, including mobilephones, tablets, laptop computers, desktop computers, set top boxes,personal digital assistants, vehicle navigation systems, and so forth.

System 100 enables efficient streaming and presentation of contentdespite unique characteristics of cellular networks, such as bandwidthun-predictability over short periods of time due to handover or fadingeffects. In one embodiment, system 100 can deliver the content to one ormore end user devices 101 utilizing various delivery processes includingend-to-end video streaming such as adaptive bit rate streaming. Theadaptive bit rate streaming can use an encoder which can encode a singlesource video at multiple bit rates. The player client can adjust betweenstreaming the different encodings depending on available resources. Inone embodiment, the streaming client can be made aware of the availablestreams at differing bit rates and segments of the streams by a manifestfile.

The adaptive bit rate streaming can be of various types including HTTPLive Streaming (HLS) and Dynamic Adaptation Streaming over HypertextTransfer Protocol (DASH). For example, system 100 can employ DASHstreaming which uses chunk-based downloading and adjusting of streamingbit rates. The streaming bit rates can be adjusted based on variousfactors including predicted available bandwidth for the end user device101, monitored buffer capacity, and/or play progress. In videostreaming, DASH can enable stitching chunks of the content together toprovide a continuous playback by the end user device 101.

System 100 can overcome inefficiencies in streaming due to conservativebandwidth estimations and/or video quality oscillation by obtainingknowledge of the network conditions from the network service provider,which can include low layer connection information such as signalinformation that is generally difficult to access from the applicationlayer without root access. The network conditions can be detailed datathat enables a more accurate prediction as compared to other processes,such as those that rely on only historical data.

In one embodiment, system 100 can enable a network service provider(e.g., a cellular network service provider), which has much betterknowledge of the connection conditions for the clients via its ownmonitoring, to selectively share network conditions and/or a predictedavailable bandwidth (for a particular end user device 101) that isdetermined from those network conditions. The sharing of thisinformation, including conditions along the last mile which can, in someinstances, become a bottleneck, enables dynamically adapting bit ratesto more efficiently stream the content to the end user device 101.

In one embodiment, cellular network service providers can also determinethe number of active users in each cell and associated with the packetscheduling system, thus allowing better predictions in terms ofavailable bandwidth. Statistics for the cells, such as radio informationand base station utilization, can be readily available to the cellularnetwork service provider and are often already being collected by thenetwork service providers such as for other purposes, including resourceallocation, handovers, and so forth.

In one embodiment, system 100 allows for the network service provider toselectively share limited information with the content provider (e.g., avideo streaming service) for adjusting bit rates during delivery ofcontent over a wireless network of the network service provider. Forexample, the network service provider can selectively allow access, bythe content provider, to a predicted available bandwidth for an end userdevice 101, and the content provider can then adjust the streamingservice decision based on the predicted available bandwidth. Thepredicted available bandwidth can be for a future time period, such asfor the next several seconds, although other time windows of bandwidthprediction can be utilized. By doing so, the video streaming service canachieve higher quality while reducing the amount of chunk prefetching,and thus save data budget and power consumption. The network serviceprovider can also benefit from less bursty traffic and can avoid orreduce excessive queuing delay from flow pacing. System 100 can providea scalable architecture to compute and expose predicted availablebandwidths (e.g., for individual end user devices) to content providersin real-time or on the fly, such as inserting the data in packetheaders, providing an API that enables selective access to detailednetwork conditions, and so forth.

System 100, via selectively sharing network conditions and/or predictedavailable bandwidth for the end user device 101, can also overcome thelack of application knowledge of the network service provider, such asknowledge of the client video buffer occupancy which would lead tore-buffering interruption in the presentation or stalls upon bufferdrain due to too high of a bit rate being selected. In one embodiment,the selective sharing by the network service provider with the contentprovider to allow the content provider to make the video streamingdecisions (e.g., adjusting of the video bit rate during the streaming)enables system 100 to overcome different users applying differentsubscription policies with respect to video quality.

In one embodiment, the selective sharing of information can include thenetwork service provider providing the content provider with access tothe predicted available bandwidth without providing access to moredetailed data of the network conditions (e.g., channel quality indicatordata, throughput data, and so forth). In one embodiment, the contentprovider can dynamically adjust bit rates during streaming of contentbased on predicted available bandwidths for the end user device withoutsharing user information with the network service provider. System 100can efficiently deliver content with high quality by dynamicallyadjusting bit rates without modification of packet scheduling systems ofthe network service provider.

System 100 can include various network elements including MobilityManagement Entity (MME) 110, Serving GateWay (SGW) 120, eNodeB (eNB)130, and Packet Data Network Gateway (PGW) 140. Various other devicescan also be utilized to facilitate establishing communications for thecommunication devices, including servers, routers, switches and soforth. The MME 110 can provide for paging and tagging proceduresincluding re-transmissions, as well as the beareractivation/deactivation process and selecting the SGW 120 for the enduser device 101. The MME 110 can perform other functions for a wirelesscommunication session, such as authentication (via a Home SubscriberServer) allocating temporary identities to the end user device 101, andthe control plane function for mobility between different accessnetworks.

The SGW 120 can route user data packets, and be a mobility anchor forthe user plane during eNB handovers and for mobility between differentaccess technologies. The SGW 120 can trigger paging when downlink dataarrives for the end user device 101 and can manage parameters of the IPbearer service and network internal routing information.

The PGW 140 can provide connectivity from the end user device 101 toexternal packet data networks. End user device 101 can have simultaneousconnectivity with more than one POW 140. The PGW 140 can perform policyenforcement, packet filtering, charging support and/or packet screening.The PGW 140 can be an anchor for mobility between 3GPP and non-3GPPtechnologies.

In one embodiment, system 100 can include a bandwidth predictor 115which can be a separate device, such as a network server, or can beresident on another network element, such as a server(s) hosting the MME110. The bandwidth predictor 115 can obtain performance data associatedwith end user device 101 and/or associated with a network cell(s) inwhich the end user device is operating or neighboring. For example, theperformance data can include Channel Quality Indicator (CQI) data,Reference Signal Received Quality (RSRQ) data, cell congestion data,cell or end user device throughput, number of active users in a cell,available resources associated with a cell, and/or user's queue size.Other performance data can also be monitored, which can be dataassociated with the particular end user device 101 and/or dataassociated with the network including the cell and/or neighboring cells.The bandwidth predictor 115 can receive the performance data directly,such as from the eNBs 130 and/or can receive it from other networkelements, such as intermediate network servers that collect performancedata from the eNBs. The bandwidth predictor 115 can monitor theperformance data for various end user devices 101 that are active in thenetwork.

In one embodiment, the mobility of the end user device 101 can bemonitored. For example, mobility data associated with the end userdevice 101 can be obtained. A mobility pattern for the end user device101 can be determined according to the mobility data and the predictedavailable bandwidth can be determined in part according to the mobilitypattern of the end user device. For example, the mobility pattern can beutilized as a predictor for future mobility of the end user device thatcan affect the network conditions, such as through a handoverrequirement. In one embodiment, the bandwidth predictor 115 can obtainperformance data by collecting data from the eNB 130 servicing the enduser device 101 and/or other eNBs of the network.

The bandwidth predictor 115 can predict an available bandwidth for theend user device 101 according to the performance data (or a portionthereof) and/or the device mobility. The available bandwidth for the enduser device 101 can be predicted over a future time period or window,such as a few seconds or over a time period corresponding to a portionof content (e.g., single chunk) to be streamed. The particular timeperiod of the prediction can vary and can include a window that is lessthan a length of the content being streamed (e.g., less than theduration of a movie). In one embodiment, the bandwidth predictor 115 cancontinue monitoring performance data and/or the device mobility, and cangenerate predictions for available bandwidth for end user devices 101 inthe network. In another embodiment, the bandwidth predictor 115 cangenerate predictions for available bandwidth for an end user device 101in response to a request to do so, such as from the end user device 101and/or from a streaming server 150 that streams content, such as videocontent, to the end user device 101.

In one embodiment, the predicted available bandwidth for the end userdevice 101 can be obtained or otherwise accessed by a video applicationto cause a video bit rate to be determined for a portion of mediacontent to be streamed to the end user device. Other factors can beutilized in determining the video bit rate including a comparison of abuffer occupancy of the end user device 101 with a buffer threshold forthe end user device so as to avoid buffer exhaustion or depletionresulting in an interruption in the presentation of the media content bythe end user device. The streaming server 150 can stream the mediacontent by applying the determined video bit rate for the particularportion of the media content. The size of the portion of media contentthat utilizes the determined video bit rate can vary. For instance inone embodiment, the determined video bit rate can be applied to a chunkof media content being streamed according to DASH. Another video bitrate can then be determined for the next chunk of media contentaccording to a subsequent predicted available bandwidth for the end userdevice 101 (as well as any other facts being utilized such as monitoringof the buffer occupancy of the end user device). This process ofaccessing predicted available bandwidths for the end user device 101,monitoring the buffer occupancy of the end user device and thendynamically adjusting the video bit rate of the media content beingstreamed to the end user device can be repeated and continued until themedia content streaming is completed.

In one embodiment, the obtaining of the predicted available bandwidthfor the end user device 101 can be via an API provided by the networkservice provider managing the bandwidth predictor 115. For example, thenetwork service provider can establish an API that enables video clientsto selectively access all or some of the detailed network conditions. Inone embodiment, the obtaining of the predicted available bandwidth forthe end user device 101 can be a selective process that requiresauthentication of the requesting party. For example, a requesting partycan be a content provider that has agreed to limited access to thepredicted available bandwidth data for the end user device, includingnot sharing the data with other parties. In one embodiment, thepredicted available bandwidth can be protected data that preventssharing and/or recording of the data by the content provider (or otherparty requesting access to the predicted available bandwidth data). Forexample, the protected data can include restrictions embedded thereinthat prevents further distribution. The monitoring of the end userdevice 101, such as by the streaming server 150, can be performed invarious ways, including requesting buffer occupancy information directlyfrom the end user device 101.

In one embodiment, system 100 can provide for integration of selectiveaccess to network performance information (e.g., predicted availablebandwidth) with a client-driven adaptation process, such as in DASHstreaming. The network performance information can be made available tothe client (e.g., video application), which can use it as a part of itsadaptation logic, and/or can share with a content server.

In one embodiment, the process of generating and exposing the predictedavailable bandwidth can start at the base station (e.g., eNB 130), wherecell load, signal statistics and user throughput are collected. Based onthe collected data, predicted throughput can be generated for all usersand reported to the bandwidth predictor 115, such as once per reportinginterval. The bandwidth predictor 115 can store this predicted availablebandwidth, which can be queried by clients. As an example, the clientscan be HTTP-based and the bandwidth predictor 115 can use an HTTP-basedAPI. Depending on requirements, clients can query the network state forthe current or past reporting periods. The bandwidth predictor 115 canfurther process information received by base stations, such as toproduce aggregated or long-term predictions. As an example, if basestations report every 500 ms, then the bandwidth predictor 115 mayconstruct a prediction for 2, 5, or any number of seconds. This maydepend on the frequency that the clients may query the bandwidthpredictor 115.

In one embodiment in an application- or service-specific scenario, onlycertain applications or services may be permitted to query the bandwidthpredictor 115. In another embodiment, all mobile clients can have accessto the predicted available bandwidth (e.g., limited to the particularend user device streaming the content). In a client-orientedarchitecture, the bandwidth predictor 115 can be located inside thecellular core network and queried by a video application via HTTP. Theprediction delivered by the bandwidth predictor 115 can be valid for thenext several seconds, such as on the order of a video chunk duration. Inone embodiment, each response may include information on how oftenclients may issue queries and for how long the returned prediction isvalid.

Multiple forms of media services can be offered via system 100 to mediadevices over landline and/or wireless technologies. System 100 canoperate according to various wireless access protocols such as GlobalSystem for Mobile or GSM, Code Division Multiple Access or CDMA, TimeDivision Multiple Access or TDMA, Universal Mobile Telecommunications orUMTS, World interoperability for Microwave or WiMAX, Software DefinedRadio or SDR, Long Term Evolution or LTE, and so on. Other present andnext generation wide area wireless access network technologies can beused in one or more embodiments of the subject disclosure.

FIG. 2 depicts an illustrative embodiment depicting a system 200 thatenables predicting available bandwidth for one or more end user devices101. The particular architecture shown is illustrative and otherarchitectures can also be implemented, including centralized ordistributed systems. In system 200, a two-tiered system is beingutilized. A proxy can be placed in the data plane, and local and centralbandwidth estimators can be placed in the control plane. For example,the local bandwidth estimator can communicate with eNBs to obtain firstperformance data such as channel quality, number of active users, cellload, users' queue size, and so forth. This first performance data canthen be utilized for computing a preliminary available bandwidth for theend user device(s). The local bandwidth estimator can be logicallydistributed in the Radio Access Network (RAN) which enables it to bescaled-out more easily.

In this example, a central bandwidth estimator can be positioned in thecore network. The central bandwidth estimator can receive and analyzethe preliminary bandwidth estimations from the local bandwidthestimator(s), in conjunction with second performance data, such as MMEmobility information, to compute the final bandwidth prediction for theend user device. In some instances, a single local estimation can beutilized to determine the final estimation, and in other instances, suchas when a handover is occurring, several local estimations can beutilized in the analysis. In this example, the central bandwidthestimator can be scaled-out with MMEs in the network. System 200 can beutilized with various network architectures, including 3G and 4Gnetworks. Once the computation work is finished, the central bandwidthestimator sends the predicted available bandwidth to the proxy which canselectively allow access to the predicted available bandwidth by thirdparties such as by a content provider (e.g., via an API accessed by avideo streaming server).

System 200 enables determining predicted available bandwidth for anynumber of end user devices 101 according to various network performancedata. The predicted available bandwidth data can then be selectivelyaccessed to enable dynamic adjustment of bit rates utilized in streamingmedia content to a corresponding one of the end user devices. Theprediction windows can be of various sizes (e.g., a few seconds) and theadjustments to the bit rates can be applied to various sizes of portionsof the media content, such as adjustments to bit rates for each orseveral chunks of the media content being streamed according to DASH.

FIG. 3 illustrates a datagram 300 that enables exposing Key PerformanceIndicators (KPIs) (e.g., predicted available bandwidth) to a party otherthan the network service provider (e.g., to a content provider). Byenabling access to this information, other parties, such as associatedwith video clients, can improve communication services, includingadaptive bit rate streaming of media content. The exposed KPIs can belimited to the predicted available bandwidth (with or without theperformance data used for the calculation) or can include otherinformation that facilitates streaming of the media content. In thisexample, the predicted available bandwidth can be encoded in either orboth of the option field and sequence number. Cellular networks candeploy proxies in the core network for pacing purposes where the proxysplits the TCP connection into two: the server communicating with theproxy and the proxy communicating with the client. By exposing the KPIsvia the proxy to the server, security can be more robust and networkcongestion can be reduced. The downstream link carries a majority of thetraffic, while the upstream link has only a small fraction of thetraffic for feedback data. The server may send the KPIs back to theclient's video play application.

Enabling access to the KPIs (e.g., predicted available bandwidth) via aninline method rather than a new connection does not result in doublingthe number of connections between the server and the proxy, and alsoprovides the video streaming connection and metric exposure connectionbeing directed to different private servers in a CDN load balancer.

In one embodiment, the KPIs (e.g., the predicted available bandwidth)can be encoded in the sequence number field. Since the sequence numberfield has thirty-two bits, eight bits can be reserved for the KPIexposure. For instance, seven bits can be utilized for a floating numberand one bit for a handover. As an example, seven bits can be dividedinto integer and fractional parts, three bits for integer and four bitsfor fractional, providing a precision of 0.063 Mbps and a maximum valueof 8.94 Mbps, which is sufficient for video streaming bit rate selectionwith a range between 0.2 to 4 Mbps. At the same time, these seven bitscan still support 16 million packets before wrapping around. Also,enabling access to the KPIs (e.g., predicted available bandwidth) viathe sequence number, rather than a TCP option field, can avoid anymiddle-boxes on the Internet modifying or dropping the option field.

FIG. 4 depicts an illustrative embodiment of a method 400 used bysystems 100 and/or 200 for providing adaptive bit rate streaming ofcontent to end user devices where predicted available bandwidth isutilized for determining the bit rates. At 402, a request for predictedavailable bandwidth for an end user device can be transmitted to anetwork server, such as bandwidth predictor 115. The request can betransmitted by the end user device 101 and/or the streaming server 150.At 404, the predicted available bandwidth for the end user device 101can be obtained, such as accessing the predicted available bandwidth viaan API of the bandwidth predictor 115. In one embodiment, theinformation accessible can be limited to the predicted availablebandwidth without accessing network performance data from which thepredicted available bandwidth was calculated.

At 406, the buffer occupancy of a buffer of the end user device can bemonitored or otherwise determined. For example, the buffer occupancy canbe determined and compared to a buffer threshold to identify whether thebuffer occupancy is in a first zone which has a higher likelihood ofbuffer drain as compared to a second zone which has a lower likelihoodof buffer drain. The threshold can be determined based on variousfactors including the total buffer capacity, the type of media content,and so forth.

At 408, a bit rate for a portion of media content to be streamed over anetwork (e.g., a wireless cellular network) to the end user device 101can be determined according to the predicted available bandwidth andaccording to the buffer occupancy. As an example, the media content canbe streamed to the end user device 101 utilizing an adaptive bitratestreaming process (e.g., DASH, HLS, and so forth). The determined biterate can be applied at 410 during the streaming process. A determinationas to whether all of the media content has been streamed to the end userdevice can be made at 412. If additional portions of the media content(e.g., more chunks of media content) need to be streamed to the end userdevice, then method 400 can return to 402 to request a subsequentbandwidth prediction and method 400 can be repeated so that another bitrate adjustment can be made for a subsequent portion of the mediacontent to be streamed to the end user device. The method 400 cancontinue until the entire media content has been streamed to the enduser device.

In one embodiment, the accessing of the predicted available bandwidthcan include an authentication of the requestor. In another embodiment,the predicted available bandwidth can be determined according to channelquality indicator data associated with the end user device, referencesignal received quality data associated with the end user device, cellcongestion data associated with a cell of the wireless network in whichthe end user device is operating, a mobility pattern of the end userdevice, or a combination thereof.

In one embodiment, the determining of the bit rate for the portion ofthe media content to be streamed can include selecting a reference bitrate according to comparing a buffer occupancy of the end user devicewith a buffer threshold, determining a second bit rate applied duringstreaming of a previous portion of the media content to the end userdevice, and comparing the reference bit rate with the second bit rate.In another embodiment, the obtaining of the predicted availablebandwidth can include accessing bandwidth data from a network server ofa wireless network via an API.

Referring to FIGS. 5-7, a collection of network traces from a cellularservice provider show the predictability of some KPIs that can affectavailable throughput. The traces were collected for five minutes in June2014 from several locations in the United States. The data covers morethan a half-million users distributed over two thousand eNBs. In thisexample, the prediction accuracy is determined using standardstatistical tools such as Mean Absolute Percentage Error (MAPE) and MeanSquare Percentage Error (MSPE). Cumulative Forecast Error (CFE) is alsoutilized to indicate how the prediction deviates over a long duration.

Cellular networks can have high mobility which causes link quality tofluctuate. In one embodiment, Channel Quality Index (CQI) can beutilized where the CQI is reported by the end user device to inform theeNB about its signal quality. As an example, CQI can range from 0through 15 where the higher CQI indicates a higher modulation and codingscheme and a higher throughput. Reference Signal Received Quality (RSRQ)can also be utilized which is a metric for an LTE wireless channel. RSRQcan sense the load from other users in the same cell and the neighboringcells, and can be indexed in 0-34 integer numbers where a higher numberindicates a better link quality. Future RSRQ can be predicted using timeseries analysis. For example, an Auto-Regressive Integrated MovingAverage (ARIMA) model can be implemented to predict the next second: thealgorithm can fit the best ARIMA with historical data, and it can use asliding window as the training dataset. As an example, the window sizecan be set to 15 s.

In one example, by replaying 113 sample traces where all the end userdevices are active for more than 100 seconds, an MAPE<0:18 was obtainedwith a mean of 0:064. CFE values in this example vary more across userswith a maximum CFE=34.6, which is about three times of a unit value. Inother words, for 100 seconds of prediction look-ahead, the predictionresult is 97% accurate over a long term and 82% accurate on average.This indicates that a prediction model, such as based on ARIMA, canoffer fairly accurate prediction.

In this example, the number of active users in each cell was measuredfrom the same trace as described above. The trace was collected fromRSRQ reports from end user devices to eNBs. A user was assumed to beactive in a certain second if the user reports RSRQ in a one-secondinterval. The ARIMA model was then applied in this example, and adetermination as made that in most cases a random-walk model fits best.Different cells have different loada, and the prediction was moreaccurate in a heavily loaded cell. In a proportional fair scheduler,over the long term, the bandwidth each user receives can be proportionalto the inverse value of the number of users, so the inverse value wastaken to compute the MAPE. For heavily loaded cells (>30 end userdevices), the MAPE<0:1; while for medium loaded cells (5-30 UEs), theMAPE<0:3. This indicated that 70% accuracy was achieved for eachprediction on average for a medium to heavily loaded cell.

Referring to FIGS. 8-9, a video rate selection process 900 can beimplemented that utilizes a prediction of available future bandwidth foran end user device that will be streamed the content. The process canutilize various parameters which are shown in FIG. 8. Process 900 seeksto improve or optimize performance metrics for video streaming servicesincluding quality, stability and interruption/rebuffering. In oneembodiment, since the video download is chunk-based, a video session canconsist of chunks with different qualities (bit rates). The video playercan download the chunk with the highest possible quality based onavailable bandwidth at each download point. Quality can be defined asthe ratio of ‘total size of downloaded and played video’ and ‘sum ofbandwidth.’ From a viewer's perspective, frequent rate switches can bedistracting and undesirable. One goal of process 900 is to reduce orminimize instability, defined as the number of rate switches betweenconsecutive chunks, Φ=Σ_(t)(R^(t)!=R^(t-1)), where 1 is an indicatorfunction. It is intended by process 900 that video play not be haltedfor rebuffering video chunks, otherwise user experience will bedegraded. Video play interruptions affect the user engagement andresults in early abandonment of the video play. Process 900 can minimizethe number of interruptions over the entire session.

For evaluating process 900, an offline algorithm, OPT, was created thatreceived available bandwidth during the entire session as an input andselects video quality for each chunk. The performance of the OPTalgorithm forms an upper bound on possible gains from knowledge offuture bandwidth. Given the available bandwidth for the entire videoplay session, optimal video rates for each chunk can be determined. AMixed Integer Linear Program (MILP) can be formulated with the goal ofmaximizing quality without causing any interruptions. When implementingprocess 900, chunks are needed ahead of their actual playing timeincluding requiring one chunk before starting the video presentation.

Utilizing a first rate selection algorithm, to ensure no interruptions,at each chunk, the total download should not exceed the sum ofcapacities until that video chunk:

 The dowload at time t is given by Download =${\sum\limits_{i = 1}^{M}\; {R_{i}*{x_{i}^{t}.\mspace{14mu} {The}}\mspace{14mu} {zero}\text{-}{interruptions}\mspace{14mu} {constraint}\mspace{14mu} {is}\mspace{14mu} {\sum\limits_{t = 1}^{\tau}\; \left( {\sum\limits_{i = 1}^{M}\; {R_{i}*x_{i}^{t}}} \right)}}} \leq$${\sum\limits_{t = 1}^{\tau}\; {C^{t}\mspace{14mu} {for}\mspace{14mu} {\forall{\tau \in {{\left\{ {1,\ldots,T} \right\}.\mspace{14mu} {As}}\mspace{14mu} {stated}\mspace{14mu} {earlier}}}}}},$quality (play efficiency) is the overall utilization of the availablebandwidth over time period [1:T], defined as$E = {{\frac{\sum\limits_{t = 1}^{\tau}\; \left( {\sum\limits_{i = 1}^{M}\; {R_{i}*x_{i}^{t}}} \right)}{\sum\limits_{t = 1}^{\tau}\; C^{t}}.\mspace{14mu} {The}}\mspace{14mu} {MILP}\mspace{14mu} {for}\mspace{14mu} {maximizing}\mspace{14mu} {quality}}$without causing interruption is   MAXIMIZE                    E  SUBJECT TO   ${\forall{\tau \in \left\{ {1,\ldots,T} \right\}}},{{\sum\limits_{t = 1}^{\tau}\; \left( {\sum\limits_{i = 1}^{M}\; {R_{i}*x_{i}^{t}}} \right)} \leq {\sum\limits_{t = 1}^{\tau}\; C^{t}}}$   ${\forall t},{{\sum\limits_{i = 1}^{M}\; x_{i}^{t}} = 1}$   ∀t, i,x_(i) ^(t) = {0, 1}

Process 900 can select a video rate for each chunk based on predictionsof available bandwidth in the near future. Process 900 can use thepredicted bandwidth for the next video chunk interval to compute areference play bit rate, and can use this referred bandwidth, playhistory and buffer occupancy to make a final decision. In oneembodiment, process 900 knows the available bandwidth forecast for asmall time interval, such as for the next chunk. Process 900 can be ajoint optimization solution based on both the video buffer occupancy andthe predicted bandwidth. In deriving process 900, the algorithm can bemotivated with a naive solution: pick the maximum play bit rate that canbe supported by the known future bandwidth. The stability may beimpacted due to highly variable bandwidth values, and because all of thebandwidth for maximizing quality of the video is being utilized, thereis a good chance that the playout buffer will not have many chunks.

Connection loss (e.g., zero or near zero bandwidth) of several secondscan occur in cellular networks and these may drain out the buffer andresult in video interruption and re-buffering. To avoid this, the buffercan be categorized or otherwise divided into multiple zones such as twozones—risky and safe—based on buffer occupancy threshold B_(risk). Othernumbers of zones can be used such as three zones such as safe, transientand risky as described below in “Can Accurate Predictions Improve VideoStreaming In Cellular Networks.”

As an example, in the risky zone, a low buffer occupancy exists and ahigh chance of buffer over-drain. An attempt to build up the buffer canbe employed by picking one rate lower than what the available bandwidthcan support. For example, if the bandwidth is 0:58 Mbps and the twoclosest video bit rates are 0:56 and 0:375, 0:375 can be chosen as anideal bit rate for the bandwidth in the risky zone in order to fill inthe client buffer to safe phase faster. In the safe zone, the highestavailable bit rate can be selected which is 0:56 Mbps in this example.This computed bit rate can be the reference bit rate, Rref. Process 900considers the stability in the following manner: it compares thereference bit rate and current bit rate (e.g., bit rate applied toprevious streaming), and chooses the reference or current bandwidth or aplay bit rate between them based on a certain criteria. Process 900 canuse delayed updates based on the buffer occupancy status. At a highlevel, process 900 attempts to stay at Rcur, unless either the bandwidthand recent buffer gains cannot sustain that play bit rate causing theprocess 900 to adjust down to lower quality, or the near futurebandwidth is higher and recent buffer gains can sustain a potentialbuffer loss if the bandwidth returns to a current bandwidth in a farfuture so that the process adjusts up to higher quality.

If Rref<Rcur, since the current play bit rate is higher than availablebandwidth, the process 900 can select the current play bit rate at thecost of draining the client video buffer. If the buffer size is in safezone, the process 900 can take the risk and can adhere to the currentplay bit rate. If the buffer is in risky zone, the process 900 cancompute the possible buffer drain if choosing the current play bit rateBufferLoss=ChunkDuration*(Rcur−C)/C, because it takesChunkDuration*Rcur/C to finish downloading ChunkDuration seconds ofvideo. The buffer change can be compared in the last k chunks where k isa tunable parameter (k=5 in evaluation), B=Bt−Bt-k+ChunkDuration*k,where Bt represents the buffer occupancy at the end of tth chunkdownload. If the buffer change in the last k chunks is positive andsufficient to compensate the buffer drain for next one video chunk, theprocess 900 chooses the current play bit rate, otherwise it chooses thehighest bit rate which can suffice this criterion. This step can avoidvideo presentation interruption while ensuring stability.

Utilizing the first rate selection algorithm, if Rref>Rcur, though thereferred play bit rate is higher, the high available bandwidth may befor a short period of time and the process 900 may need to check thebuffer gain in the last few chunks against potential buffer loss. Inaddition to that, if the buffer occupancy is high, the process 900 canbe more aggressive and adjust up faster and vice versa. To add thisfactor, the potential buffer loss is computed for k′ chunks where k′ isin linear relation to unfilled buffer B′=z−B, k′=α*B′/ChunkDuration,where a is another tunable knob (α=0:15 in the evaluation). Aconservative case can be assumed in the future where the network hasenough bandwidth to stream at current bit rate with no client bufferloss or gain, so potential BufferLoss=α*B′*(Rref−Rcur)/Rcur. If thevalue is less than buffer gain, a determination can be made to overdrainthe buffer with the new play bit rate. This step can boost stability.The process 900 can continuously predict or otherwise estimate thebandwidth for the next chunk and can select the bit rate and downloadonce a chunk finishes downloading, unless the client buffer is totallyfull at size z, as shown in FIG. 9. Use of a different algorithm fordynamically selecting of the video bit rate resulted in differentresults which are described below in “Can Accurate Predictions ImproveVideo Streaming In Cellular Networks.”

Referring to FIGS. 10-13, process 900 was evaluated based on variousmetrics including quality, stability and interruption (rebuffering). Theavailable bandwidth traces were taken from one major U.S. 4G networkcarrier and MIT Sprout Project. Wireless channel traces were cut into5-10 minute long segments, as 80-90% video sessions are shorter than 10minutes. In total there are four 3G and 12 4G segments. Ten differentvideo encoding rates are taken from one major video streaming carrier:{0:235; 0:375; 0:56 . . . 4:3} Mbps and it was assumed there was novariation in bit rates for each chunk. Two different streamingalgorithms were selected for comparison against process 900: FESTIVE andBBA. FESTIVE was selected because its algorithm is designed for thescenario where there are several users sharing a single bottleneck linkduring video streaming session, which fits the cellular network settingwhere the wireless channel can be the bottleneck. FESTIVE takes theharmonic mean of bandwidth over the last 20 video chunk downloads asreference and adds randomization into video chunk download scheduling toavoid a synchronized congestion. BBA relies on video buffer occupancy asan implicit feedback of the network condition for video bit rateselection, and uses a proportional relation to the buffer occupancy tocompute the desired rate. BBA also has a fast startup phase where itramps up to a desirable play bit rate based on buffer occupancy growthrate. Throughout the comparison with FESTIVE, the tunable knob was setfor stability α=12, bandwidth factor p=1 and recommended buffer size 60seconds. In BBA, BufferReservoir=90 s, BufferCushion=126 s and maxbuffer z=240 s. The risky phase Brisk was set to 60 s and max bufferz=240 s, look back chunk size k=5 and =0:15. Each video chunk is 4seconds, which is a prevalent length for one major video streamingvendor.

To illustrate the comparison of process 900 of the first rate selectionalgorithm with FESTIVE and BBA, an “oracle” estimator is utilized whichindicates the space of improvement for the metrics being compared:quality (play efficiency), stability and interrupts. As shown in FIG.10, process 900 outperforms the other two algorithms in all traces interms of all three metrics except against FESTIVE for stability in two3G traces. For the traces where FESTIVE achieves higher stability,FESTIVE tends to stay at low play bit rates and thus has much lowerquality and link utilization. Further, in these traces, the stabilityissue is less important: process 900 has 5.5 switches on average whileFESTIVE has 3 switches, and in all other traces all algorithms have >6switches. Prefetched buffer size was also compared between BBA andprocess 900. FESTIVE is not a client video buffer based solution andonly adds randomized scheduling if it reaches a certain bufferlevel—that buffer level was extended to z=240 s download and there wasno difference seen in terms of FESTIVE's efficiency, stability orinterruption, since FESTIVE only looks at the past 20 chunks. Comparingto BBA, process 900 has reduced buffer size significantly by 10-40seconds even though both maximum buffer sizes z=240. One sample trace isillustrated in FIG. 12: ideally the download progress and play progressare on the red line, however due to prefetching the download time isalways ahead of play time, or the download line is always above the redline and the gap is the client play buffer size.

For the first rate selection algorithm, a synthetic trace was used totest the properties of fast ramp up and resilience to connection loss.The bandwidth was set to static at 4.3 Mbps to test ramp-up time. 30, 45and 60 seconds connection outage after 100 seconds 4.3 Mbps bandwidthwas added to test resilience. There are zero interruptions in all cases;process 900 and FESTIVE's play bit rates are 4.3 Mbps at the moment,while BBA's is 1.75 Mbps. A difference exists between play time anddownload time, where FESTIVE takes 260 s play time to get to 4.3 Mbps,but at 100 s download time FESTIVE is downloading video chunk at bitrate 4.3 Mbps, and the gap is due to prefetching. There is a drop to alower quality in some instances which is illustrated in FIG. 13. Process900 has a good performance margin in both ramp-up time and resilience tolink loss as compared to FESTIVE and BBA.

As a comparison with a theoretical maximum quality, 16 traces werereplayed and the results show that process 900 can achieve 84% of theefficiency upper bound described by the MILP algorithm above.

Process 900 also performs well in a noisy prediction scenario. To showthis, a synthetic estimator is utilized for prediction and when theprediction is roughly accurate with a certain degree of error rate,process 900 is robust enough to achieve a close-to-optimal performance.A sensitive study for process 900 was conducted with the noisyestimator. The synthetic estimator uses a noisy factor with adistribution of Gauss (1, 0.2) or Uniform (0.5, 1.5) when offeringpredicted bandwidth. Through repetitive replay, process 900 wasdetermined to achieve about the same performance in all traces, but witharound a 10% chance of degradation in stability (1.5× quality switches)in the repetitive replay. Use of a different algorithm for dynamicallyselecting of the video bit rate resulted in different results which aredescribed below in “Can Accurate Predictions Improve Video Streaming InCellular Networks.”

FIG. 14 depicts an illustrative embodiment of a communication device1400. Communication device 1400 can serve in whole or in part as anillustrative embodiment of the devices depicted in systems 100 and 200and can be configured to perform portions of method 400. As an example,communication device 1400 can obtain a predicted available bandwidth,determine a buffer occupancy threshold, determine a buffer occupancy,compare the buffer occupancy with the buffer threshold, and determine avideo bit rate for a portion of media content according to the predictedavailable bandwidth and according to the comparing of the bufferoccupancy with the buffer threshold. The video bit rate can then beapplied during streaming of the portion of the media content over awireless network. This process can be repeated until the streaming ofthe media content is completed. The predicted available bandwidth can bedetermined according to channel quality indicator data and/or accordingto reference signal received quality data. The obtaining of thepredicted available bandwidth can include: transmitting a request forthe predicted available bandwidth and, responsive to an authentication,receiving the predicted available bandwidth from a network server of thewireless network. The predicted available bandwidth can be determinedaccording to cell congestion data associated with a cell of the wirelessnetwork in which the end user device is operating. The predictedavailable bandwidth can be determined according to a mobility pattern.The predicted available bandwidth can be determined according toprediction information collected by a network server from an eNodeB,where the prediction information includes channel quality, a number ofactive users, a cell load, and throughput data. The determining of thevideo bit rate for the portion of the media content can includecomparing of the buffer occupancy with the buffer thresholds,determining a current video bit rate applied during streaming of aprevious portion of the media content over the wireless network, andpredicted available bandwidth.

Communication device 1400 can comprise a wireline and/or wirelesstransceiver 1402 (herein transceiver 1402), a user interface (UI) 1404,a power supply 1414, a location receiver 1416, a motion sensor 1418, anorientation sensor 1420, and a controller 1406 for managing operationsthereof. The transceiver 1402 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 1402 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 1404 can include a depressible or touch-sensitive keypad 1408with a navigation mechanism such as a roller ball, a joystick, a mouse,or a navigation disk for manipulating operations of the communicationdevice 1400. The keypad 1408 can be an integral part of a housingassembly of the communication device 1400 or an independent deviceoperably coupled thereto by a tethered wireline interface (such as a USBcable) or a wireless interface supporting for example Bluetooth®. Thekeypad 1408 can represent a numeric keypad commonly used by phones,and/or a QWERTY keypad with alphanumeric keys. The UI 1404 can furtherinclude a display 1410 such as monochrome or color LCD (Liquid CrystalDisplay), OLED (Organic Light Emitting Diode) or other suitable displaytechnology for conveying images to an end user of the communicationdevice 1400. In an embodiment where the display 1410 is touch-sensitive,a portion or all of the keypad 1408 can be presented by way of thedisplay 1410 with navigation features.

The display 1410 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 1400 can be adapted to present a user interfacewith graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The touch screen display 1410 can beequipped with capacitive, resistive or other forms of sensing technologyto detect how much surface area of a user's finger has been placed on aportion of the touch screen display. This sensing information can beused to control the manipulation of the GUI elements or other functionsof the user interface. The display 1410 can be an integral part of thehousing assembly of the communication device 1400 or an independentdevice communicatively coupled thereto by a tethered wireline interface(such as a cable) or a wireless interface.

The UI 1404 can also include an audio system 1412 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 1412 can further include amicrophone for receiving audible signals of an end user. The audiosystem 1412 can also be used for voice recognition applications. The UI1404 can further include an image sensor 1413 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 1414 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 1400 to facilitatelong-range or short-range portable applications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 1416 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 1400 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor1418 can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 1400 in three-dimensional space. Theorientation sensor 1420 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device1400 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 1400 can use the transceiver 1402 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 1406 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 1400.

Other components not shown in FIG. 14 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 1400 can include a reset button (not shown). The reset button canbe used to reset the controller 1406 of the communication device 1400.In yet another embodiment, the communication device 1400 can alsoinclude a factory default setting button positioned, for example, belowa small hole in a housing assembly of the communication device 1400 toforce the communication device 1400 to re-establish factory settings. Inthis embodiment, a user can use a protruding object such as a pen orpaper clip tip to reach into the hole and depress the default settingbutton. The communication device 1400 can also include a slot for addingor removing an identity module such as a Subscriber Identity Module(SIM) card. SIM cards can be used for identifying subscriber services,executing programs, storing subscriber data, and so forth.

The communication device 1400 as described herein can operate with moreor less of the circuit components shown in FIG. 14. These variantembodiments can be used in one or more embodiments of the subjectdisclosure.

The communication device 1400 can be adapted to perform the functions ofthe end user device 101, the bandwidth predictor 115, the eNB 130, theserver 150, and other devices of system 100, as well as the localbandwidth estimator, the central bandwidth estimator, the proxy andother devices of system 200. It will be appreciated that thecommunication device 1400 can also represent other devices that canoperate in systems 100 and 200 such as a gaming console and a mediaplayer. In addition, the controller 1406 can be adapted in variousembodiments to perform the functions 1475. Functions 1475 can includecollecting various performance data and predicting available bandwidthfor end user device(s) over a future time period. Functions 1475 canalso include dynamically determining bit rates according to predictedavailable bandwidth and buffer occupancy.

Upon reviewing the aforementioned embodiments, it would be evident to anartisan with ordinary skill in the art that said embodiments can bemodified, reduced, or enhanced without departing from the scope of theclaims described below. For example, predicted available bandwidths canbe applied to all active end user devices in the network or to a subsetof those end user devices, such as based on a subscription plan, anindication that video streaming is desired, a type of content beingstreamed, present network conditions, and so forth. In one embodiment,the future time period over which the predicted available bandwidthapplies can be varied, such as having shorter prediction time periodswhere larger fluctuations in network conditions are being predicted andhaving larger prediction time periods where smaller fluctuations innetwork conditions are being predicted. In another embodiment, thegenerating of predicted available bandwidths can be performednon-continuously. For instance, a determination can be made that networkconditions have changed only a small amount, such as below a thresholdfor a subset of network metrics being monitored, and based on thisdetermination the previous predicted available bandwidth can be deemedvalid for the subsequent future time period.

In another embodiment, the content provider can monitor characteristicsof the end user device, such as monitoring the buffer occupancy of theend user device, and can determine the video bit rates for streaming ofthe content according to the predicted available bandwidth data, as wellas according to the monitoring of the end user device. Other embodimentscan be used in the subject disclosure.

It should be understood that devices described in the exemplaryembodiments can be in communication with each other via various wirelessand/or wired methodologies. The methodologies can be links that aredescribed as coupled, connected and so forth, which can includeunidirectional and/or bidirectional communication over wireless pathsand/or wired paths that utilize one or more of various protocols ormethodologies, where the coupling and/or connection can be direct (e.g.,no intervening processing device) and/or indirect (e.g., an intermediaryprocessing device such as a router).

FIG. 15 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 1500 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as the bandwidth predictor 115, the server 150,the end user device 101 and other devices of FIGS. 1-2 to enable dynamicadjustment of bit rates during streaming according to predictedavailable bandwidth and according to buffer occupancy.

In some embodiments, the machine may be connected (e.g., using a network1526) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server or a client user machine in aserver-client user network environment, or as a peer machine in apeer-to-peer (or distributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

The computer system 1500 may include a processor (or controller) 1502(e.g., a central processing unit (CPU)), a graphics processing unit(GPU, or both), a main memory 1504 and a static memory 1506, whichcommunicate with each other via a bus 1508. The computer system 1500 mayfurther include a display unit 1510 (e.g., a liquid crystal display(LCD), a flat panel, or a solid state display). The computer system 1500may include an input device 1512 (e.g., a keyboard), a cursor controldevice 1514 (e.g., a mouse), a disk drive unit 1516, a signal generationdevice 1518 (e.g., a speaker or remote control) and a network interfacedevice 1520. In distributed environments, the embodiments described inthe subject disclosure can be adapted to utilize multiple display units1510 controlled by two or more computer systems 1500. In thisconfiguration, presentations described by the subject disclosure may inpart be shown in a first of the display units 1510, while the remainingportion is presented in a second of the display units 1510.

The disk drive unit 1516 may include a tangible computer-readablestorage medium 1522 on which is stored one or more sets of instructions(e.g., software 1524) embodying any one or more of the methods orfunctions described herein, including those methods illustrated above.The instructions 1524 may also reside, completely or at least partially,within the main memory 1504, the static memory 1506, and/or within theprocessor 1502 during execution thereof by the computer system 1500. Themain memory 1504 and the processor 1502 also may constitute tangiblecomputer-readable storage media.

The exemplary embodiments can also employ other processes to dynamicallyadjust a bit rate during streaming of media content including a class ofPrediction-Based Adaptation (PBA) algorithms that use the predictedbandwidth. Existing algorithms fail to fully utilize availablebandwidth, achieving only 69%-86% of optimal quality. The performancegap is pronounced during startup where existing algorithms achieve only15%-20% of optimal in the first 32 seconds, and 22%-38% of optimal inthe first 64 seconds. Naive algorithms that utilize only predictedbandwidth for chunk rate selection do not work well and cause numerousand erratic quality switches. PBA that combines short-term predictions(e.g., one chunk duration) with buffer occupancy and/or rate stabilityfunction outperforms existing algorithms, achieving nearly 96% ofoptimal quality during startup and over the entire video, therebyoutperforming existing algorithms by up to about 40%. PBA algorithmswith different stability functions can trade off stalls and stabilitywhile maintaining much improved average quality. Improvement can beachieved in video QoE by accurately predicting available bandwidth andexposing them to content providers through APIs.

The available bandwidth in both wired and wireless networks can varyover time. To overcome this variability, the industry has shiftedtowards ABR streaming to deliver videos over the Internet. ABR streamingis a client-driven approach in which a video client tries to match thedelivery rate of the video to that of the available end-to-endbandwidth. To make this feasible, ABR streaming breaks videos into“chunks” of a few seconds (typically 2-10 sec) and encodes each chunk atmultiple bit rates, representing different levels of quality. Multipleencoding bit rates also directly correlate to PSNR levels resulting fromcompressing the video signal. The client's task is to choose chunks ofthe “correct” encoding rate. Depending on the perceived networkconditions, playout buffer or other criteria, clients can attempt tooptimize various metrics that comprise users' QoE, including quality,interruptions and stability. For example, a client can switch to alow-quality version of the video to avoid buffer undertow duringtemporary network congestion, and switch back to higher quality afternetwork conditions improve. Netflix, Microsoft Smooth Streaming, AppleHTTP Live Streaming (HLS) and Dynamic Adaptive Streaming over HTTP(DASH) have been deployed to in commercial systems to deliver content.While simple in principle, practical client implementations of ABRstreaming have to infer the available bandwidth and adjust chunkencoding rates accordingly. State-of-the-art approaches typically usepast observations to estimate available bandwidth. For example, FESTIVEis an adaptation algorithm that includes features intended to providefair and stable performance even when multiple ABR video players competefor bandwidth. Among other features, FESTIVE uses harmonic mean ofprevious chunk throughputs and includes a stability function that delaysvideo rate updates to minimize rate switches. In contrast, Buffer-BasedAdaptation (BBA) is proposed to dispense with historical averaging andsolely use buffer level to drive selection of video rates. The rationaleis that buffer level indirectly reflects the historical throughput,which obviates a need for direct measurement and estimation. However,when the buffer is empty or very low, such as during the startup phase,historical bandwidth estimation is necessary.

The inaccurate inference of available bandwidth leads to inaccurateestimates of actual bandwidth, which results in low video QoE overcellular networks. The exemplary embodiments may not need to utilizesynthetic traces. Instead, real traces may be utilized. Bandwidthprediction according to the exemplary embodiments can complement orreplace existing algorithms to improve video QoE.

In a second rate selection algorithm, the video session can be dividedinto time intervals of length equal to chunk duration where knowledge ofthe available average bandwidth, during each chunk duration, is assumed.The highest possible quality for this session can be sought such thatthere are no interruptions.

While this may be an idealized setting, it provides an upper bound onthe video quality possible and allows quantification of how wellexisting adaptation algorithms are performing. This optimal rateselection can be formulated as a Mixed Integer Linear Program (MILP).Table 1600 lists the variables and parameters used in the formulation.For the i-th video chunk, the optimization selects exactly one qualityfor playback. In other words, the indicator variable x^(q) _(i) is onefor exactly one quality level q and the chunk size is given by

${{Chunk}\mspace{14mu} {{Size}(i)}} = {\sum\limits_{q = 1}^{Q}\; {R_{i}^{q}*{x_{i}^{q}.}}}$

For each video chunk i, the optimization needs to find its quality level(x^(q) _(i)) and the goal is to maximize

Σ_(i)ChunkSize(i)=Σ_(i,q) R _(i) ^(q) *x _(i) ^(q),

subject to constraints defined below.

Unique quality: One quality can be selected for each chunk:

${\forall i},{{\sum\limits_{q = 1}^{Q}\; x_{i}^{q}} = 1.}$

No stalls: To avoid interruptions, the i-th chunk can finish downloadingby the end of chunk duration i to be played out in chunk duration i+1.In other words, we cannot download any portion of the i-th chunk in anychunk duration indexed higher than i:

∀i,j>i,L _(i) ^(j)=0.

Limited buffer: We do not want to download too many chunks ahead oftheir play time because, in case of abandonment, these chunks representwasted network bandwidth. For this reason, most practicalimplementations have a limited buffer size. This is captured byrestricting that a chunk cannot get downloaded M chunk durations aheadof its play time:

∀i,j≦i−M,L _(i) ^(j)=0

Bandwidth availability: The predicted bandwidth in the j-th chunkduration, Cj, should be sufficient to download all (portions of) chunksdownloaded in the j-th duration:

∀j,Σ _(i) L _(i) ^(j) ≦C _(j).

Download consistency: Portions of i-th chunk downloaded in various chunkdurations can sum up to the size of the i-th chunk:

∀i,Σ _(j) L _(i) ^(j)≧Σ_(i,q) R _(i) ^(q) *x _(i) ^(q).

There may not be constraints to force download of chunks in order andthe solution may interleave the downloading of video chunks (e.g.,download chunks 1 and 3 in the 1st chunk duration and then downloadchunk 2 in the 2nd chunk duration). It can be shown that given such aschedule, it can be transformed into a sensible schedule where chunksare downloaded in sequence without violating any of the constraints.

Other adaptation algorithms can be compared to the MILP using 20 4G/LTEcellular traces that were collected from several large US cellularproviders. Each trace provides the per-second available bandwidth over360 seconds. To accurately represent some of the more challengingconditions to deliver ABR video, the traces were obtained from variouslocations with different terrains (highways, local roads, etc.) andrepresent situations where bandwidth is more constrained and fluctuatingquickly due to changing cellular conditions. The average bandwidthacross traces is 6.5 Mbps. It can be assumed that each chunk is 4seconds long and a maximum buffer of 64 seconds (or equivalently, 16chunks). 10 different video encoding rates can be taken from one majorcontent provider: 235, 375, 560, 750, 1050, 1750, 2350, 3000, 3850, and4300 Kbps and assume constant bit rate for each chunk. The selectedchunk duration and buffer size can approximate common settings ofseveral content providers for wired and wireless environments. A fewtraces could not satisfy MILP's strict no-stall constraint. MILP wassolved on the remaining traces using a solver and was compared with theaverage bit rate to those of FESTIVE and BBA.

Full FESTIVE was implemented with recommended stability parameters andBBA-2 variant. Over the full traces of 360 sec (90 chunks), on average,FESTIVE and BBA achieve 68.6% and 85.7% of optimal, respectively, as inFIG. 17. Using a re-run of MILP to maximize the rate during short andlong start-up phases of 32 and 64 seconds, it was determined that theachievable gains obtained by MILP are significantly higher. FESTIVE andBBA can reach only 15.0% and 20.1% of optimal for 32 sec, and 21.8% and33.4% for 64 sec, respectively. These results show that significantbenefits can be gained by prediction, especially during startup.

Online versions of PBA were developed by relaxing the assumption ofperfect bandwidth knowledge for the entire session duration to shorterprediction horizons. A naive PBA selects the next chunk's quality basedonly on predicted bandwidth. This turns out to have unintendedconsequences. Then, a more sophisticated online PBA algorithm wasdeveloped that obtains significant improvements in quality by combiningavailable bandwidth prediction for only the next few seconds (e.g., onechunk duration) with buffer occupancy or rate stability function.

The exemplary algorithm can be motivated with a naive solution: choosethe highest bit rate that is less than the predicted bandwidth. Theprediction horizons are short (1 chunk duration of 4 seconds), medium (5chunks), and long (10 chunks). The client obtains a prediction justbefore each chunk download. It can be assumed that the future availablebandwidth is known for a fixed time period and represented by a singleaverage value.

Evaluation: FIGS. 18-20 show the three behaviors for three predictionhorizons. The video rate selection based on 1-chunk horizon closelyfollows predicted bandwidth, leading to numerous (39) and erraticquality switches (FIG. 18). This greedy rate selection is clearlyreflected in very slow buffer filling, and no use of the buffer tostabilize quality.

FIG. 19 shows a 5-chunk horizon (20 seconds), which stabilizes quality(18 switches) by averaging predicted bandwidth over a longer period, andleads to better buffer use. However, in some instances, the buffer isnot appropriately used, i.e., it is refilled while bandwidth has dropped(around 120 s), and drained while bandwidth is increasing (around 40seconds).

Finally, FIG. 20 shows another extreme by taking a 10 chunk horizon (40seconds), further stabilizing quality to 14 switches. However, thiscauses stalls (a cumulative stall time of 7.2 sec), because thelong-term average does not include information about bandwidthvariability and outages within the prediction horizon. In addition,buffer level is not considered during startup to recognize the danger ofundertow due to high bandwidth variability. While the average qualitybetween three scenarios is similar (3.05 to 3.34 Mbps), the QoE differssignificantly. There are several shortcomings of the sole use ofprediction to determine quality: (i) stability is heavily impacted byhighly fluctuating bandwidth, (ii) buffer fills slowly risking undertowbecause most of the bandwidth is used to maximize quality, and (iii)even when buffer level is high, it may not be used appropriately withrespect to bandwidth fluctuations. These shortcomings can be takeawaysto drive the design of prediction-based adaptation algorithm thatimproves video QoE, by considering stability, stalls, and bufferoccupancy.

Existing adaptation algorithms are not designed to use prediction. Forthe second rate selection algorithm, the exemplary PBA uses prediction,explicitly considers buffer occupancy and aggressively tries tostabilize rate selection, as illustrated in FIG. 21. The buffer can bedivided into three zones {safe, transient, and risky} based on twobuffer occupancy thresholds, Bsafe and Brisky. The safe and riskythresholds can be set to 90% and 30% of buffer size Bmax, respectively.A decision on the video rate for the next chunk, Rnext, can be madedifferently depending on the zone in which the current buffer occupancyB is, predicted available bandwidth C, and last video rate downloadedRlast. Algorithm 2100 of FIG. 21 provides the pseudo-code. The algorithmstarts by checking the buffer occupancy against the target maximum Bmax,and waits in case there is not enough space to store the next chunk ofduration D, thereby protecting from buffer overrun. Once buffer space isavailable, the first step is to select a reference video rate, Rref, tobe the highest available rate below predicted bandwidth C. In the riskyzone, a conservative approach is used to prevent stalls and replenishthe buffer, by first reducing Rref by one level. If Rref is higher thanthe rate of last chunk, Rlast, an upswitch to Rref can be performed.However, if Rref is lower than Rlast, this means that the reduction isrecommended. To avoid sudden quality drop to Rref, the highest rate Rcan be selected without draining the buffer to less than two chunks,which performs better than one chunk when variable chunk sizes are used.B=D is the number of chunks in the buffer before requesting next chunk,C=R is the additional number of chunks that will be downloaded, while −1represents one chunk that is expected to be played from the buffer whilethe next chunk is retrieved. During startup, with B=0, this constraintselects the rate of the first chunk equal to ⅓rd of the predictedbandwidth. If such a rate cannot be found, the only decision that can bemade is to select the lowest rate R1.

In the safe zone, the algorithm aggressively selects the larger of Rrefand Rlast to maintain stability and ride out short-term bandwidthvariations. In the transient zone, various approaches can be taken tobalance the buffer occupancy, high quality, stability, and otherobjectives. In this implementation, a more aggressive approach can betaken. When Rref is lower than Rlast, Rlast can be utilized with theexpectation that bandwidth will recover. If Rref is higher than Rlast,it is desired to grow the buffer.

The algorithm can select rate Rref if downloading at Rref will allow atleast 15% of the empty buffer space to be filled. Otherwise, a reductionby one rate can be performed and select Rref−1.

Evaluation: FIG. 22 for the second rate selection algorithm shows for aspecific trace how PBA behaves when buffer level is taken intoconsideration. Compared to naive PBA, the buffer is filled faster duringstartup, which is desirable. When the buffer level is moderate to high,it is aggressively exploited to ride out short fade durations andmaintain high quality (100-140 seconds and 260-280 seconds). The averagerate over 360 sec is 3.15 Mbps with no stalls and only 6 rate switches.Therefore, average quality is similar to naive PBA, but withsignificantly improved stability and appropriate buffer usage.

Next, PBA can be compared to FESTIVE and BBA. In addition to theexemplary PBA algorithm, an alternative is implemented using a knownstability function employed by FESTIVE, called “delayed update”. Usingthe aforementioned data set, simulations can be run to investigate howclose to optimal each adaptation algorithm gets. For the second rateselection algorithm FIG. 23 lists algorithms and the percentage ofoptimal (MILP) they can achieve using a 64-second buffer. Recall thatover the entire trace durations, FESTIVE and BBA come within 68.6% and85.7% of optimal, respectively. PBA with buffer-based stability function(PBA-BB) reaches 95.8% of optimal quality, representing a relativeimprovement of about 40% over FESTIVE. PBA with delayed update (PBA-DU)is within 91.4% of optimal quality. During startup phase (32 s), thevalue of prediction becomes abundantly clear, with up to 95.8% ofoptimal quality, while FESTIVE and BBA remain extremely sub-optimal.Comparing metrics other than video quality shows a slight advantage ofBBA because of fewer stalls. This is not surprising given that BBA isdesigned specifically to avoid stalls, unlike PBA and FESTIVE. WhileFESTIVE and BBA are nearly as stable as optimal, PBA-BB registers thebest stability, and PBA-DU reduces stalls by decreasing stability, whichis a very desirable trade-off. PBA algorithms with different stabilityfunctions can trade off stalls and stability while maintaining muchimproved average quality. The benefit of prediction can be quantified,i.e., to show that the benefits come from prediction and not from otherparts of the adaptation algorithm. This is not a straight-forward task,since every algorithm is designed to work best with a specific bandwidthestimate. Nevertheless, a benchmark is illustrated using the exemplaryadaptation algorithm with two stability functions and swap predictedbandwidth with output of other heuristics. This approach maintains theadaptation process the same except how the reference rate is computed. Aharmonic mean of last 10 chunks and last chunk throughput can beutilized as the two heuristics, and 4-second (1 chunk) look-ahead forPBA. Harmonic mean is the core feature of FESTIVE and last chunkthroughput is used by BBA during startup. For the second rate selectionalgorithm FIG. 24 shows the relative improvement that prediction (PBA)offers compared to other heuristics for a given stability function.Several observations can be made. First, when looking at the average bitrates over the entire trace of 360 sec, all algorithms do well.Prediction provides up to 4.8% improvement on average quality whilemaintaining overall better number of stalls and switching statistics.Second, during the “startup” phase, prediction offers nearly 18%improvement in video rate compared to harmonic mean. Finally, out of thethree, last chunk strategy performs the worst in terms of stall andswitch statistics. On the other hand, harmonic-based strategy hascommensurate stall and switching statistics.

With “delayed update”, prediction outperforms others by a similar marginduring startup phase. Prediction offers significant reduction in thenumber of stalls and the cumulative time stalled. However, this comes atthe expense of an increased number of switches. Reducing the number ofstalls is a desirable feature in terms of customer engagement. Finally,PBA is stall-free in 6 more traces than harmonic (a 25% improvement).

While the results show that accurate predictions of available networkbandwidth improve the quality of video over cellular networks,generating accurate predictions is non-trivial. The cellular radio linkis carefully scheduled. A base station tracks multiple network metricsand uses them in the scheduling process. In addition, the architectureof the network allows an operator to have a view of all devices, theirlink statistics, radio spectrum availability, mobility patterns, andinstantaneous traffic demand. It is sometimes possible to get veryaccurate predictions (98% accuracy) for short time periods (500 msec)just by observing network performance at a stationary client device.Good mobility models and feasible throughput predictions over short timeperiods (5 seconds) can be obtained even when users are mobile. Thisindicates that it is feasible for cellular operators to predictavailable bandwidth accurately. In one exemplary embodiment, multiplepredictions (e.g., a very accurate short term prediction and anindicative long term prediction) or even a distribution of bandwidthover the predicted period can be utilized.

In one embodiment, a throughput guidance protocol can be utilized wherebandwidth information is embedded in TCP headers. This approach makesthe information available to TCP stacks of senders, to be potentiallyused by all applications. In another embodiment, the cellular providercan expose bandwidth predictions through an API that applications queryeach time they start downloading a video chunk.

The exemplary embodiments provide for collecting and analyzing variousnetwork information including user mobility, channel quality formillions of users and generating predictions in real time to be usefulfor video rate selection (and other possible applications). In oneembodiment, a system can partition the network into small areas andindependently compute predictions for each area.

A cellular operator typically has access to a range of in-networkmeasurements that can be potentially used for making short to mid-termbandwidth predictions. Leveraging bandwidth predictions whenimplementing video adaptation algorithms can significantly improve videoQoE. During startup, the exemplary PBA algorithm can deliver more than4× better video quality compared to heuristic-based algorithms. This isimportant since it has been shown that users typically abandon in thefirst few minutes of the video if the picture quality suffers. A naivealgorithm that solely uses prediction does not do well and that acombination of prediction with buffer occupancy and/or stabilityfunction can be used to extract the maximum benefit. PBA algorithms withdifferent stability functions can trade off stalls and stability whilemaintaining much improved average quality.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Application specific integrated circuits andprogrammable logic array can use downloadable instructions for executingstate machines and/or circuit configurations to implement embodiments ofthe subject disclosure. Applications that may include the apparatus andsystems of various embodiments broadly include a variety of electronicand computer systems. Some embodiments implement functions in two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals communicated between and through the modules,or as portions of an application-specific integrated circuit. Thus, theexample system is applicable to software, firmware, and hardwareimplementations.

In accordance with various embodiments of the subject disclosure, theoperations or methods described herein are intended for operation assoftware programs or instructions running on or executed by a computerprocessor or other computing device, and which may include other formsof instructions manifested as a state machine implemented with logiccomponents in an application specific integrated circuit or fieldprogrammable gate array. Furthermore, software implementations (e.g.,software programs, instructions, etc.) including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein. It is furthernoted that a computing device such as a processor, a controller, a statemachine or other suitable device for executing instructions to performoperations or methods may perform such operations directly or indirectlyby way of one or more intermediate devices directed by the computingdevice.

While the tangible computer-readable storage medium 1522 is shown in anexample embodiment to be a single medium, the term “tangiblecomputer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “tangible computer-readable storage medium” shallalso be taken to include any non-transitory medium that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of the methods ofthe subject disclosure. The term “non-transitory” as in a non-transitorycomputer-readable storage includes without limitation memories, drives,devices and anything tangible but not a signal per se.

The term “tangible computer-readable storage medium” shall accordinglybe taken to include, but not be limited to: solid-state memories such asa memory card or other package that houses one or more read-only(non-volatile) memories, random access memories, or other re-writable(volatile) memories, a magneto-optical or optical medium such as a diskor tape, or other tangible media which can be used to store information.Accordingly, the disclosure is considered to include any one or more ofa tangible computer-readable storage medium, as listed herein andincluding art-recognized equivalents and successor media, in which thesoftware implementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) representexamples of the state of the art. Such standards are from time-to-timesuperseded by faster or more efficient equivalents having essentiallythe same functions. Wireless standards for device detection (e.g.,RFID), short-range communications (e.g., Bluetooth®, WiFi, Zigbee®), andlong-range communications (e.g., WiMAX, GSM, CDMA, LTE) can be used bycomputer system 1500.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Theexemplary embodiments can include combinations of features and/or stepsfrom multiple embodiments. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. Figuresare also merely representational and may not be drawn to scale. Certainproportions thereof may be exaggerated, while others may be minimized.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

Less than all of the steps or functions described with respect to theexemplary processes or methods can also be performed in one or more ofthe exemplary embodiments. Further, the use of numerical terms todescribe a device, component, step or function, such as first, second,third, and so forth, is not intended to describe an order or functionunless expressly stated so. The use of the terms first, second, thirdand so forth, is generally to distinguish between devices, components,steps or functions unless expressly stated otherwise. Additionally, oneor more devices or components described with respect to the exemplaryembodiments can facilitate one or more functions, where the facilitating(e.g., facilitating access or facilitating establishing a connection)can include less than every step needed to perform the function or caninclude all of the steps needed to perform the function.

In one or more embodiments, a processor (which can include a controlleror circuit) has been described that performs various functions. Itshould be understood that the processor can be multiple processors,which can include distributed processors or parallel processors in asingle machine or multiple machines. The processor can be used insupporting a virtual processing environment. The virtual processingenvironment may support one or more virtual machines representingcomputers, servers, or other computing devices. In such virtualmachines, components such as microprocessors and storage devices may bevirtualized or logically represented. The processor can include a statemachine, application specific integrated circuit, and/or programmablegate array including a Field PGA. In one or more embodiments, when aprocessor executes instructions to perform “operations”, this caninclude the processor performing the operations directly and/orfacilitating, directing, or cooperating with another device or componentto perform the operations.

The Abstract of the Disclosure is provided with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, it can beseen that various features are grouped together in a single embodimentfor the purpose of streamlining the disclosure. This method ofdisclosure is not to be interpreted as reflecting an intention that theclaimed embodiments require more features than are expressly recited ineach claim. Rather, as the following claims reflect, inventive subjectmatter lies in less than all features of a single disclosed embodiment.Thus the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separately claimedsubject matter.

What is claimed is:
 1. A machine-readable storage medium, comprisingexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: obtaining a predicted availablebandwidth; determining one or more buffer occupancy thresholds;determining a buffer occupancy; comparing the buffer occupancy with theone or more buffer thresholds; determining a prior video bit rate of aprevious portion of media content; and determining a video bit rate fora portion of media content according to the predicted availablebandwidth and according to the comparing of the buffer occupancy withthe one or more buffer thresholds, wherein the video bit rate is appliedduring streaming of the portion of the media content over a wirelessnetwork.
 2. The machine-readable storage medium of claim 1, wherein theoperations further comprise: obtaining a second predicted availablebandwidth; determining a second buffer occupancy of the buffer,comparing the second buffer occupancy with the buffer threshold;determining a second video bit rate for a second portion of the mediacontent according to the second predicted available bandwidth andaccording to the comparing of the second buffer occupancy with thebuffer threshold; and applying the second video bit rate duringstreaming of the second portion of the media content over the wirelessnetwork.
 3. The machine-readable storage medium of claim 2, wherein thepredicted available bandwidth is determined according to channel qualityindicator data, and according to reference signal received quality dataassociated.
 4. The machine-readable storage medium of claim 1, whereinthe obtaining of the predicted available bandwidth comprises:transmitting a request for the predicted available bandwidth; andresponsive to an authentication of the processor, receiving thepredicted available bandwidth from a network server of the wirelessnetwork.
 5. The machine-readable storage medium of claim 1, wherein thepredicted available bandwidth is determined according to cell congestiondata associated with a cell of the wireless network.
 6. Themachine-readable storage medium of claim 1, wherein the predictedavailable bandwidth is determined according to a mobility pattern. 7.The machine-readable storage medium of claim 1, wherein the streaming ofthe portion of the media content over the wireless network is accordingto a dynamic adaptation streaming over HTTP protocol, and wherein thepredicted available bandwidth is for a time period covering a chunk ofthe media content and less than an entirety of the media content.
 8. Themachine-readable storage medium of claim 1, wherein the obtaining of thepredicted available bandwidth comprises accessing bandwidth data from anetwork server of the wireless network via an application programminginterface.
 9. The machine-readable storage medium of claim 1, whereinthe predicted available bandwidth is determined according to predictioninformation collected by a network server from an eNodeB, wherein theprediction information includes channel quality, a number of activeusers, a cell load, and throughput data.
 10. The machine-readablestorage medium of claim 1, wherein the determining of the video bit ratefor the portion of the media content comprises: selecting a referencevideo bit rate according to the comparing of the buffer occupancy withthe buffer threshold, determining a current video bit rate appliedduring streaming of a previous portion of the media content over thewireless network, and comparing the reference video bit rate with thecurrent video bit rate.
 11. A method comprising: obtaining, by a systemincluding a network server, first performance data associated with anend user device and second performance data associated with a cell of awireless network in which the end user device is operating, wherein thefirst performance data includes channel quality indicator data,reference signal received quality data or a combination thereof, andwherein the second performance data includes cell congestion data;determining, by the system, a predicted available bandwidth for the enduser device according to the first and second performance data;receiving, by the system from a processor, a request for the predictedavailable bandwidth for the end user device; providing, by the system tothe processor, the predicted available bandwidth for the end user deviceto cause a video bit rate to be determined for a portion of mediacontent according to the predicted available bandwidth for the end userdevice and according to a comparison of a buffer occupancy of the enduser device with a buffer threshold for the end user device; andfacilitating, by the system over the wireless network, streaming of theportion of the media content to the end user device utilizing the videobit rate.
 12. The method of claim 11, wherein the providing of thepredicted available bandwidth for the end user device to the processoris responsive to a determination that the processor is permitted toaccess available bandwidth data.
 13. The method of claim 11, comprising:obtaining mobility data associated with the end user device; anddetermining a mobility pattern for the end user device, wherein thepredicted available bandwidth is determined according to the mobilitypattern of the end user device.
 14. The method of claim 11, wherein thestreaming of the portion of the media content to the end user deviceover the wireless network is according to a dynamic adaptation streamingover HTTP protocol, and wherein the predicted available bandwidth is fora time period covering one or more chunks of the media content and lessthan an entirety of the media content.
 15. The method of claim 11,wherein the obtaining of the first and second performance data comprisescollecting, by the system, data from an eNodeB, and wherein the cellcongestion data includes throughput data and a number of active usersassociated with the cell.
 16. A system, comprising: a processor; and amemory that stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: obtainingpredicted available bandwidths for an end user device; monitoring bufferoccupancy of a buffer of the end user device; determining bit rates forportions of media content according to the predicted availablebandwidths and according to the buffer occupancy; and adjusting bitrates for portions of media content according to the predicted availablebandwidths and according to the buffer occupancy during streaming of themedia content to the end user device.
 17. The system of claim 16,wherein the obtaining of the predicted available bandwidths for the enduser device comprises: transmitting, to a network server, a request forthe predicted available bandwidths for the end user device; andresponsive to an authentication of the system, receiving, from thenetwork server, the predicted available bandwidths, wherein thepredicted available bandwidths are determined according to channelquality indicator data associated with the end user device, referencesignal received quality data associated with the end user device, cellcongestion data associated with a cell of a wireless network in whichthe end user device is operating, a mobility pattern of the end userdevice, or a combination thereof.
 18. The system of claim 16, whereinthe streaming of the media content to the end user device is accordingto a dynamic adaptation streaming over HTTP protocol.
 19. The system ofclaim 16, wherein the determining of the bit rates for the portions ofthe media content comprises: selecting a reference bit rate according tocomparing the buffer occupancy with a buffer threshold, determining asecond bit rate applied during streaming of a previous portion of themedia content to the end user device, and comparing the reference bitrate with the second bit rate.
 20. The system of claim 16, wherein theobtaining of the predicted available bandwidths for the end user devicecomprises accessing bandwidth data from a network server via anapplication programming interface, and wherein the portions of the mediacontent correspond to chunks of the media content according to a dynamicadaptation streaming over HTTP protocol.